docstr: 将InputFeatures转换为Tensor
  Args:
      features (List[InputFeatures])
  Returns:
      tensors (List[Tensors])
docstr_tok: ["\u5c06InputFeatures\u8f6c\u6362\u4e3aTensor","Args",":","features","(","List","[","InputFeatures","]",")","Returns",":","tensors","(","List","[","Tensors","]",")"]
code: def collate_fn_test(features):
      """将InputFeatures转换为Tensor
      Args:
          features (List[InputFeatures])
      Returns:
          tensors (List[Tensors])
      """
      input_ids = torch.tensor(
          [f.input_ids for f in features], dtype=torch.long
      )
      attention_mask = torch.tensor(
          [f.attention_mask for f in features], dtype=torch.long
      )
      triples = [f.triples for f in features]
      input_tokens = [f.input_tokens for f in features]
      tensors = [input_ids, attention_mask, triples, input_tokens]
      return tensors
  
file_path: DeepKE/src/deepke/triple_extraction/PRGC/dataloader.py
imports: import os
  import json
  import torch
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Dataset
  from transformers import BertTokenizer
  from .dataloader_utils import read_examples, convert_examples_to_features
label_desc: Use image node_id 3 for calling the CustomDataLoader obj's underlying member method code with example usage: obj.collate_fn_test(features) and returns: tensors